ABSTRACT Cardiovascular diseases (CVDs) represent the leading cause of death in the US and are costly to the nation. Despite ongoing prevention efforts, socially and economically disadvantaged populations continue to bear the highest burden of CVDs. There is growing evidence that social determinants of health can play a role in producing such disparities in CVD. Yet current interventions designed to reduce disparities in CVD seldom focus on the social determinants of cardiovascular health across multiple domains and levels of influence but tend to focus on individual-level factors. Furthermore, when designing interventions, policymakers have to consider the trade-offs between interventions that maximize health in the general population and those that minimize health disparities and failure to reach a balance between these can result in increased health disparities and suboptimal population health. Therefore, to efficiently reduce disparities in CVD?coronary heart diseases and stroke, there is a pressing need for assisting policy decision-making in the design of sustainable and affordable targeted health interventions that integrate social factors across domains and levels of influence and which targets the socially and economically disadvantaged populations. This K01 proposal will apply several analytic approaches including key informant interviews, the synthetic control method and simulation modeling and systems science (SMSS) methods to evaluate the impact and costs of intervention strategies that have the potential to efficiently reduce CVD health disparities. To carry out this proposal, Dr. Nianogo will (1) identify interventions and policies that are implemented in California which focused on the social determinants of cardiovascular health across multiple domains and levels of influence, (2) investigate the impact of three societal policy interventions on CVD incidence; (3) develop a computer simulation model to project the long-term impact and cost-effectiveness of interventions and policies targeting disadvantaged subpopulations, high-risk subpopulations. Findings from this study help identify targeted health interventions that have the potential to reduce health disparities in CVD and inform policy decision-making. This K01 proposal builds on the candidate?s previous research that developed a computer simulation model (Virtual Los Angeles Cohort?ViLA) to study the prevalence and incidence of obesity and type 2 diabetes among a representative sample of U.S. persons born in Los Angeles and followed from birth to age 65. The proposal will be pursued within the context of a strong mentorship that has adequate extensive experience and expertise on intervention research, CVDs, health disparities and simulation modeling that is necessary for the success of this project. UCLA provides an adequate environment to conduct such innovative research. Throughout this proposal, the candidate will gain skills in SMSS and CVD disparities research; these skills are critical to providing Dr. Nianogo with the means and knowledge to become a successful independent investigator in computational epidemiology and SMSS and to secure future R01 funding.